Startseite Statistical methods for in silico tools used for risk assessment and toxicology
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Statistical methods for in silico tools used for risk assessment and toxicology

  • Nermin A. Osman ORCID logo EMAIL logo
Veröffentlicht/Copyright: 7. Januar 2022
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

In silico toxicology is one type of toxicity assessment that uses computational methods to visualize, analyze, simulate, and predict the toxicity of chemicals. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. Animal studies for the type of toxicological information needed are both expensive and time-consuming, and to that, ethical consideration is added. Many different types of in silico methods have been developed to characterize the toxicity of chemical materials and predict their catastrophic consequences to humans and the environment. In light of European legislation such as Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) and the Cosmetics Regulation, in silico methods for predicting chemical toxicity have become increasingly important and used extensively worldwide e.g., in the USA, Canada, Japan, and Australia. A popular problem, concerning these methods, is the deficiency of the necessary data for assessing the hazards. REACH has called for increased use of in silico tools for non-testing data as structure-activity relationships, quantitative structure-activity relationships, and read-across. The main objective of the review is to refine the use of in silico tools in a risk assessment context of industrial chemicals.


Corresponding author: Nermin A. Osman, Department of Biomedical Informatics and Medical Statistics, Alexandria University Medical Research Institute, 165 El-Horria Avenue, Alexandria, 21561, Egypt, E-mail:

  1. Author contributions: The author has accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The author declares no conflicts of interest regarding this article.

References

1. Koruga, D. Ultimate computing: biomolecular consciousness and nanotechnology. Biosystems 1988;22:83–4. https://doi.org/10.1016/0303-2647(88)90052-4.Suche in Google Scholar

2. Breville, M. US environmental protection agency tribal environmental health research program. Epidemiology 2011;22:S115. https://doi.org/10.1097/01.ede.0000392021.64753.75.Suche in Google Scholar

3. Hartung, T, Hoffmann, S. Food for thought on in silico methods in toxicology. ALTEX 2009;36:155–66. https://doi.org/10.14573/altex.2009.3.155.Suche in Google Scholar PubMed

4. Ekins, S, Mestres, J, Testa, B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 2007;152:9–20. https://doi.org/10.1038/sj.bjp.0707305.Suche in Google Scholar PubMed PubMed Central

5. Ekins, S, Mestres, J, Testa, B. In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol 2007;152:21–37. https://doi.org/10.1038/sj.bjp.0707305.Suche in Google Scholar

6. Muster, W, Breidenbach, A, Fischer, H, Kirchner, S, Müller, L, Pähler, A. Computational toxicology in drug development. Drug Discov Today 2008;13:303–10. https://doi.org/10.1016/j.drudis.2007.12.007.Suche in Google Scholar PubMed

7. Tennekes, H. Novel approaches to chemical risk assessment. Environ Risk Assess Remediat 2017;3:S1. https://doi.org/10.4066/2529-8046.1000e101.Suche in Google Scholar

8. Kortagere, S, Krasowski, M, Ekins, S. The importance of discerning shape in molecular pharmacology. Trends Pharmacol Sci 2009;30:138–47. https://doi.org/10.1016/j.tips.2008.12.001.Suche in Google Scholar PubMed PubMed Central

9. Valerio, LJr. In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol 2009;241:356–70. https://doi.org/10.1016/j.taap.2009.08.022.Suche in Google Scholar PubMed

10. Merlot, C. Computational toxicology—a tool for early safety evaluation. Drug Discov Today 2010;15:16–22. https://doi.org/10.1016/j.drudis.2009.09.010.Suche in Google Scholar PubMed

11. Worth, A. The future of in silico chemical safety … and beyond. Comput Toxicol 2019;10:60–2. https://doi.org/10.1016/j.comtox.2018.12.005.Suche in Google Scholar

12. Stenner, R, Kees Van Leeuwen, Theo Vermeire (Eds.): Risk assessment of chemicals—an introduction. Environ Sci Pollut Res 2008;15:450–1. https://doi.org/10.1007/s11356-008-0017-0.Suche in Google Scholar

13. Myatt, G, Bower, D, Cross, K, Hasselgren, C, Miller, S, Quigley, D. In silico toxicology protocols and software platforms. Toxicol Lett 2017;280:S286. https://doi.org/10.1016/j.toxlet.2017.07.802.Suche in Google Scholar

14. Ma, J, Tong, C, Liaw, A, Sheridan, R, Szumiloski, J, Svetnik, V. Generating hypotheses about molecular structure-activity relationships (SARs) by solving an optimization problem. Stat Anal Data Min: ASA Data Sci J 2009;2:161–74. https://doi.org/10.1002/sam.10040.Suche in Google Scholar

15. Gao, G. Statistical modeling of SAR images: a survey. Sensors 2010;10:775–95. https://doi.org/10.3390/s100100775.Suche in Google Scholar PubMed PubMed Central

16. Gupta-Ostermann, D, Shanmugasundaram, V, Bajorath, J. Neighborhood-based prediction of novel active compounds from SAR matrices. J Chem Inf Model 2014;54:801–9. https://doi.org/10.1021/ci5000483.Suche in Google Scholar PubMed

17. Roy, K, Kar, S, Das, R. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment, 2nd ed. Amsterdam: Academic Press, an imprint of Elsevier; 2015.Suche in Google Scholar

18. Gramatica, P. Principles of QSAR modeling. Int J Quant Struct-Property Relat 2020;5:61–97. https://doi.org/10.4018/ijqspr.20200701.oa1.Suche in Google Scholar

19. Gramatica, P. Principles of QSAR models validation: internal and external. QSAR Comb Sci 2007;26:694–701. https://doi.org/10.1002/qsar.200610151.Suche in Google Scholar

20. Migut, M, Worring, M. Visual exploration of classification models for various data types in risk assessment. Inf Visual 2012;11:237–51. https://doi.org/10.1177/1473871611433715.Suche in Google Scholar

21. Brereton, R, Lloyd, G. Partial least squares discriminant analysis: taking the magic away. J Chemom 2014;28:213–25. https://doi.org/10.1002/cem.2609.Suche in Google Scholar

22. Salvador-Meneses, J, Ruiz-Chavez, Z, Garcia-Rodriguez, J. Compressed kNN: K-nearest neighbors with data compression. Entropy 2019;21:234. https://doi.org/10.3390/e21030234.Suche in Google Scholar PubMed PubMed Central

23. Kumar, R. Signature verification using support vector machine (SVM). Int J Sci Res Manag 2017;5:5327–30. https://doi.org/10.18535/ijsrm/v5i5.07.Suche in Google Scholar

24. Kovari, A, Andersson, N, Bell, D, Cartlidge, G, Fedtke, N, Kojo, A, et al.. Read-across in REACH and the read-across assessment framework (RAAF). Toxicol Lett 2018;295:S9. https://doi.org/10.1016/j.toxlet.2018.06.035.Suche in Google Scholar

25. Benfenati, E, Chaudhry, Q, Gini, G, Dorne, J. Integrating in silico models and read-across methods for predicting toxicity of chemicals: a step-wise strategy. Environ Int 2019;131:105060. https://doi.org/10.1016/j.envint.2019.105060.Suche in Google Scholar PubMed

26. Cherkasov, A, Muratov, E, Fourches, D, Varnek, A, Baskin, I, Cronin, M, et al.. QSAR modeling: where have you been? Where are you going to? J Med Chem 2014;57:4977–5010. https://doi.org/10.1021/jm4004285.Suche in Google Scholar PubMed PubMed Central

27. Muratov, E, Bajorath, J, Sheridan, R, Tetko, I, Filimonov, D, Poroikov, V, et al.. QSAR without borders. Chem Soc Rev 2020;49:3525–64. https://doi.org/10.1039/D0CS00098A.Suche in Google Scholar PubMed PubMed Central

28. Matthews, E, Contrera, J. In silico approaches to explore toxicity end points: issues and concerns for estimating human health effects. Expet Opin Drug Metabol Toxicol 2007;3:125–34. https://doi.org/10.1517/17425255.3.1.125.Suche in Google Scholar PubMed

29. Raies, AB, Bajic, VB. In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci 2016;6:147–72. https://doi.org/10.1002/wcms.1240.Suche in Google Scholar PubMed PubMed Central

30. Raunio, H. In silico toxicology – non-testing methods. Front Pharmacol 2011;2:33. https://doi.org/10.3389/fphar.2011.00033.Suche in Google Scholar PubMed PubMed Central

31. Basilevsky, A. The ratio estimator and maximum-likelihood weighted least squares regression. Qual Quantity 1980;14:377–95. https://doi.org/10.1007/bf00144097.Suche in Google Scholar

32. Fletcher, J. Multiple linear regression. BMJ 2009;338:b167. https://doi.org/10.1136/bmj.b167.Suche in Google Scholar

33. Nagy, G. Sector based linear regression, a new robust method for the multiple linear regression. Acta Cybern 2018;23:1017–38. https://doi.org/10.14232/actacyb.23.4.2018.3.Suche in Google Scholar

34. Beran, R. Prediction in random coefficient regression. J Stat Plann Inference 1995;43:205–13. https://doi.org/10.1016/0378-3758(94)00020-v.Suche in Google Scholar

35. Stoltzfus, J. Logistic regression: a brief primer. Acad Emerg Med 2011;18:1099–104. https://doi.org/10.1111/j.1553-2712.2011.01185.x.Suche in Google Scholar PubMed

36. Zhang, Z. Residuals and regression diagnostics: focusing on logistic regression. Ann Transl Med 2016;4:195–6. https://doi.org/10.21037/atm.2016.03.36.Suche in Google Scholar PubMed PubMed Central

37. Rynkiewicz, J. General bound of overfitting for MLP regression models. Neurocomputing 2012;90:106–10. https://doi.org/10.1016/j.neucom.2011.11.028.Suche in Google Scholar

38. Kumar, R. Errors in use of multivariable regression analysis. Indian J Pharmacol 2015;47:571–2. https://doi.org/10.4103/0253-7613.165187.Suche in Google Scholar PubMed PubMed Central

39. Senaviratna, NA, Cooray, T. Diagnosing ulticollinearity of logistic regression model. Asian J Probab Stat 2019;2:1–9. https://doi.org/10.9734/ajpas/2019/v5i230132.Suche in Google Scholar

40. Hansen, M, Cai, L, Monroe, S, Li, Z. Limited-information goodness-of-fit testing of diagnostic classification item response models. Br J Math Stat Psychol 2016;69:225–52. https://doi.org/10.1111/bmsp.12074.Suche in Google Scholar PubMed

41. Kartoun, U. A glimpse of the difference between predictive modeling and classification modeling. J Clin Epidemiol 2019;109:142. https://doi.org/10.1016/j.jclinepi.2019.01.001.Suche in Google Scholar PubMed

42. Krupinski, E. Receiver operating characteristic (ROC) analysis. Frontline Learn Res 2017;5:31–42. https://doi.org/10.14786/flr.v5i2.250.Suche in Google Scholar

43. Topliss, J, Edwards, R. Chance factors in studies of quantitative structure-activity relationships. J Med Chem 1979;22:1238–44. https://doi.org/10.1021/jm00196a017.Suche in Google Scholar PubMed

44. Heinze, G, Wallisch, C, Dunkler, D. Variable selection – a review and recommendations for the practicing statistician. Biom J 2018;60:431–49. https://doi.org/10.1002/bimj.201700067.Suche in Google Scholar PubMed PubMed Central

45. Cheng, F, Ikenaga, Y, Zhou, Y, Yu, Y, Li, W, Shen, J, et al.. In silico assessment of chemical biodegradability. J Chem Inf Model 2012;52:655–69. https://doi.org/10.1021/ci200622d.Suche in Google Scholar PubMed

Published Online: 2022-01-07

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Reviews
  3. Magnetic characterization of magnetoactive elastomers containing magnetic hard particles using first-order reversal curve analysis
  4. Microscopic understanding of particle-matrix interaction in magnetic hybrid materials by element-specific spectroscopy
  5. Biodeinking: an eco-friendly alternative for chemicals based recycled fiber processing
  6. Bio-based polyurethane aqueous dispersions
  7. Cellulose-based polymers
  8. Biodegradable shape-memory polymers and composites
  9. Natural substances in cancer—do they work?
  10. Personalized and targeted therapies
  11. Identification of potential histone deacetylase inhibitory biflavonoids from Garcinia kola (Guttiferae) using in silico protein-ligand interaction
  12. Chemical computational approaches for optimization of effective surfactants in enhanced oil recovery
  13. Social media and learning in an era of coronavirus among chemistry students in tertiary institutions in Rivers State
  14. Techniques for the detection and quantification of emerging contaminants
  15. Occurrence, fate, and toxicity of emerging contaminants in a diverse ecosystem
  16. Updates on the versatile quinoline heterocycles as anticancer agents
  17. Trends in microbial degradation and bioremediation of emerging contaminants
  18. Power to the city: Assessing the rooftop solar photovoltaic potential in multiple cities of Ecuador
  19. Phytoremediation as an effective tool to handle emerging contaminants
  20. Recent advances and prospects for industrial waste management and product recovery for environmental appliances: a review
  21. Integrating multi-objective superstructure optimization and multi-criteria assessment: a novel methodology for sustainable process design
  22. A conversation on the quartic equation of the secular determinant of methylenecyclopropene
  23. Recent developments in the synthesis and anti-cancer activity of acridine and xanthine-based molecules
  24. An overview of in silico methods used in the design of VEGFR-2 inhibitors as anticancer agents
  25. Fragment based drug design
  26. Advances in heterocycles as DNA intercalating cancer drugs
  27. Systems biology–the transformative approach to integrate sciences across disciplines
  28. Pharmaceutical interest of in-silico approaches
  29. Membrane technologies for sports supplementation
  30. Fused pyrrolo-pyridines and pyrrolo-(iso)quinoline as anticancer agents
  31. Membrane applications in the food industry
  32. Membrane techniques in the production of beverages
  33. Statistical methods for in silico tools used for risk assessment and toxicology
  34. Dicarbonyl compounds in the synthesis of heterocycles under green conditions
  35. Green synthesis of triazolo-nucleoside conjugates via azide–alkyne C–N bond formation
  36. Anaerobic digestion fundamentals, challenges, and technological advances
  37. Survival is the driver for adaptation: safety engineering changed the future, security engineering prevented disasters and transition engineering navigates the pathway to the climate-safe future
Heruntergeladen am 22.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/psr-2018-0166/html
Button zum nach oben scrollen